Unequal weight planar prediction

ABSTRACT

A method of decoding JVET video includes receiving a bitstream and calculating a final planar prediction in planar mode to predict pixel values for a current coding block. The final planar prediction may rely on using unequal weights applied to each of a horizontal and vertical predictor, where such predictors may be generated by interpolating neighboring pixels for each predicted pixel within a coding block. The computation may be made more accurate by deriving a value for a bottom right neighboring pixel.

CLAIM OF PRIORITY

This Application claims priority under 35 U.S.C. § 119(e) from earlierfiled United States Provisional Application Ser. No. 62/439,724, filedDec. 28, 2016, from earlier filed U.S. Provisional Application Ser. No.62/440,379, filed Dec. 29, 2016, from earlier filed U.S. ProvisionalApplication Ser. No. 62/459,797, filed Feb. 16, 2017, from earlier filedU.S. Provisional Application Ser. No. 62/522,420, filed Jun. 20, 2017,and from earlier filed U.S. Provisional Application Ser. No. 62/482,178,filed Apr. 5, 2017 each of which is hereby incorporated by reference.

BACKGROUND

The technical improvements in evolving video coding standards illustratethe trend of increasing coding efficiency to enable higher bit-rates,higher resolutions, and better video quality. The Joint VideoExploration Team is developing a new video coding scheme referred to asJVET. Similar to other video coding schemes like HEVC (High EfficiencyVideo Coding), JVET is a block-based hybrid spatial and temporalpredictive coding scheme. However, relative to HEVC, JVET includes manymodifications to bitstream structure, syntax, constraints, and mappingfor the generation of decoded pictures. JVET has been implemented inJoint Exploration Model (JEM) encoders and decoders.

SUMMARY

The present disclosure provides a method for calculating a final planarprediction in planar mode to predict pixel values for a current codingblock having height H, width W, and a top-left (TL) pixel within thecurrent coding block is defined by coordinates x=0 and y=0. The methodcomprises interpolating a horizontal predictor and a vertical predictorfor a pixel in a current coding block received in a vide bitstream. Afinal planar prediction value P(x,y) may be calculated using unequalweights applied to each of the first and second predictors. The finalplanar prediction value may be determined in accordance with P(x,y)=(A(x, y)*P_(h)(x, y)+B(x, y)*P_(v)(x, y)+c(x, y))/(D(x, y)), whereA(x, y) and B(x, y) are position dependent weighting factors for thehorizontal and vertical predictors, respectively, c(x, y) is a positiondependent rounding factor, and D(x, y) is a position dependent scalingfactor.

The present disclosure also provides an apparatus for calculating afinal planar prediction by way of the techniques disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Further details of the present invention are explained with the help ofthe attached drawings in which:

FIG. 1 depicts division of a frame into a plurality of Coding Tree Units(CTUs).

FIG. 2 depicts an exemplary partitioning of a CTU into Coding Units(CUs).

FIG. 3 depicts a quadtree plus binary tree (QTBT) representation of FIG.2's CU partitioning.

FIG. 4 depicts a simplified block diagram for CU coding in a JVETencoder.

FIG. 5 depicts possible intra prediction modes for luma components inJVET.

FIG. 6 depicts a simplified block diagram for CU coding in a JVETdecoder.

FIG. 7A depicts the horizontal predictor calculation.

FIG. 7B depicts a vertical predictor calculation.

FIG. 8 depicts an example of planar prediction plane with the JVETmethod that deviates from the flat plane.

FIG. 9 illustrates the planar prediction block generated based onequation (5), with the corner intensity values identical to an examplein FIG. 8.

FIG. 10 is an example of S[n], where sum of width and height is 256 andShiftDenom=10.

FIG. 11 illustrates another example of S[n], where the sum of the widthand height is 512 and ShiftDenom=10.

FIG. 12 illustrates another example of S[n], where sum of width andheight is 128 and ShiftDenom=10.

FIG. 13 illustrates another example of S[n], where sum of width andheight is 128 and ShiftDenom=9.

FIG. 14 depicts an embodiment of a computer system adapted and/orconfigured to process a method of CU coding.

FIG. 15A is a flow diagram that illustrates a method for calculating anintensity value of a bottom right neighboring pixel of a current codingblock.

FIG. 15B depicts a flow diagram for using the unequal weight planarprediction disclosed herein with a calculated intensity value of abottom right neighboring pixel of the current coding block.

FIG. 16 is a high-level view of a source device and destination devicethat may incorporate features of the systems and devices describedherein.

DETAILED DESCRIPTION

Digital video involves a large amount of data representing each andevery frame of a digital video sequence, or series of frames, in anuncompressed manner. Transmitting uncompressed digital video acrosscomputer networks is usually limited by bandwidth limitations, andusually requires a large amount of storage space. Encoding the digitalvideo may reduce both storage and bandwidth requirements.

Frames of a video sequence, or more specifically the coding tree unitswithin each frame, can be encoded and decoded using JVET. WET is a videocoding scheme being developed by the Joint Video Exploration Team.Versions of JVET have been implemented in JEM (Joint Exploration Model)encoders and decoders. Similar to other video coding schemes like HEVC(High Efficiency Video Coding), JVET is a block-based hybrid spatial andtemporal predictive coding scheme.

During coding with JVET, a frame is first divided into square blockscalled Coding Tree Units (CTUs) 100, as shown in FIG. 1. FIG. 1 depictsdivision of a frame into a plurality of CTUs 100. For example, CTUs 100can be blocks of 128×128 pixels. A frame can be an image in a videosequence, which may include a plurality of frames. A frame can include amatrix, or set of matrices, with pixel values representing intensitymeasures in the image. The pixel values can be defined to representcolor and brightness in full color video coding, where pixels aredivided into three channels. For example, in a YCbCr color space pixelscan have a luma value, Y, that represents gray level intensity in theimage, and two chrominance values, Cb and Cr, that represent the extentto which color differs from gray to blue and red. In other embodiments,pixel values can be represented with values in different color spaces ormodels. The resolution of the video can determine the number of pixelsin a frame. A higher resolution can mean more pixels and a betterdefinition of the image, but can also lead to higher bandwidth, storage,and transmission requirements.

FIG. 2 depicts an exemplary partitioning of a CTU 100 into CUs 102,which are the basic units of prediction in coding. Each CTU 100 in aframe can be partitioned into one or more CUs (Coding Units) 102. CUs102 can be used for prediction and transform as described below. UnlikeHEVC, in JVET the CUs 102 can be rectangular or square, and can be codedwithout further partitioning into prediction units or transform units.The CUs 102 can be as large as their root CTUs 100, or be smallersubdivisions of a root CTU 100 as small as 4×4 blocks.

In JVET, a CTU 100 can be partitioned into CUs 102 according to aquadtree plus binary tree (QTBT) scheme in which the CTU 100 can berecursively split into square blocks according to a quadtree, and thosesquare blocks can then be recursively split horizontally or verticallyaccording to binary trees. Parameters can be set to control splittingaccording to the QTBT, such as the CTU size, the minimum sizes for thequadtree and binary tree leaf nodes, the maximum size for the binarytree root node, and the maximum depth for the binary trees.

By way of a non-limiting example, FIG. 2 shows a CTU 100 partitionedinto CUs 102, with solid lines indicating quadtree splitting and dashedlines indicating binary tree splitting. As illustrated, the binarysplitting allows horizontal splitting and vertical splitting to definethe structure of the CTU and its subdivision in to CUs.

FIG. 3 shows a QTBT block structure representation of FIG. 2'spartitioning. To generate an encoded representation of a picture orimage, the video encoder may generate a set of CTUs. in FIG. 3, quadtreeroot node represents the CTU 100. To generate a coded CTU, the videoencoder may recursively perform quadtree partitioning on the coding treeblocks of a CTU to divide the coding tree blocks into coding blocks(CB)s. Thus, as used herein, a video slice (e.g., a video frame or aportion of a video frame) may be partitioned into coding blocks, whichmay also be referred to as coding units, and each such coding unit mayinclude blocks, such as luma blocks and chroma blocks, that areindependently decoded. Thus, because JEM supports flexibility for CUpartition shapes to better match local characteristics of video data,CUs may have non-square shapes and coding may take place at the CU levelor at the luma or chroma block level within the CU. Reference is madeherein to encoding or decoding a coding block, where a coding blockrepresents a block of pixels that makes up a CU or coding blocks withinthe CU.

As shown in FIG. 3, each child node in a CTU 100 in the quadtree portionrepresents one of four square blocks split from a parent square block.The square blocks represented by the quadtree leaf nodes can then bedivided zero or more times using binary trees, with the quadtree leafnodes being root nodes of the binary trees, representing the parentcoding unit that is partitioned into two child coding units. At eachlevel of the binary tree portion, a block can be divided vertically orhorizontally and symmetrically or asymmetrically. For example, a flagset to “0” may indicate that the block is symmetrically splithorizontally, while a flag set to “1” may indicate that the block issymmetrically split vertically.

After quadtree splitting and binary tree splitting, the blocksrepresented by the QTBT's leaf nodes represent the final CUs 102 to becoded, such as coding using inter prediction or intra prediction.Inter-prediction exploits temporal redundancies between differentframes. For example, temporally adjacent frames of video may includeblocks of pixels that remain substantially the same. During encoding, amotion vector may interrelate movement of blocks of pixels in one frameto a block of correlating pixels in another frame. Thus, the system neednot encode the block of pixels twice, but rather encode the block ofpixels once and provide the motion vector to predict the correlatedblock of pixels.

For intra-prediction, a frame or portion of a frame may be encodedwithout reference to pixels in other frames. Instead, intra-predictionmay exploit the spatial redundancies among blocks of pixels within aframe. For example, where spatially adjacent blocks of pixels havesimilar attributes, the coding process may reference a spatialcorrelation between adjacent blocks, exploiting the correlation byprediction of a target block based on prediction modes used in adjacentblocks.

For inter slices or slices or full frames coded with inter prediction,different partitioning structures can be used for luma and chromacomponents. For example, for an inter slice a CU 102 can have codingblocks for different color components, such as such as one luma CB andtwo chroma CBs. For intra slices or slices or full frames coded withintra prediction, the partitioning structure can be the same for lumaand chroma components. Thus, each of the CTUs 100 may comprise a codingtree block of luma samples, corresponding coding tree blocks of chromasamples, and syntax structures used to code the samples of the codingtree blocks. A CTU may comprise a single coding tree block and thesyntax structures used to code the samples. In either case, the CTU withcoding blocks may be comprised within one or more coding units, as shownby FIG. 2 and FIG. 3.

FIG. 4 depicts a simplified block diagram for CU coding in a JVETencoder. The main stages of video coding include partitioning toidentify CUs 102 as described above, followed by encoding CUs 102 usingprediction at 404 or 406, generation of a residual CU 410 at 408,transformation at 412, quantization at 416, and entropy coding at 420.The encoder and encoding process illustrated in FIG. 4 also includes adecoding process that is described in more detail below.

Given a current CU 102 (e.g., a CU prior to encoding; e.g., the originalCU to be encoded for transmission in the bitstream by generating aprediction CU) the encoder can obtain a prediction CU 402 eitherspatially using intra prediction at 404 or temporally using interprediction at 406. The basic idea of prediction coding is to transmit adifferential, or residual, signal between the original signal and aprediction for the original signal. At the receiver side, the originalsignal can be reconstructed by adding the residual and the prediction,as will be described below. Because the differential signal has a lowercorrelation than the original signal, fewer bits are needed for itstransmission.

A sequence of coding units may make up a slice, and one or more slicesmay make up a picture. A slice may include one or more slice segments,each in its own NAL unit. A slice or slice segment may include headerinformation for the slice or bitstream.

A slice, such as an entire picture or a portion of a picture, codedentirely with intra-predicted CUs can be an I slice that can be decodedwithout reference to other slices, and as such can be a possible pointwhere decoding can begin. A slice coded with at least someinter-predicted CUs can be a predictive (P) or bi-predictive (B) slicethat can be decoded based on one or more reference pictures. P slicesmay use intra-prediction and inter-prediction with previously codedslices. For example, P slices may be compressed further than theI-slices by the use of inter-prediction, but need the coding of apreviously coded slice to code them. B slices can use data from previousand/or subsequent slices for its coding, using intra-prediction orinter-prediction using an interpolated prediction from two differentframes, thus increasing the accuracy of the motion estimation process.In some cases, P slices and B slices can also or alternately be encodedusing intra block copy, in which data from other portions of the sameslice is used.

As will be discussed below, the intra prediction or inter prediction canbe performed based on reconstructed CUs 434 from previously coded CUs102, such as neighboring CUs 102 or CUs 102 in reference pictures.

When a CU 102 is coded spatially with intra prediction at 404, an intraprediction mode can be found that best predicts pixel values of the CU102 based on samples from neighboring CUs 102 in the picture.

When coding a CU's luma or chroma block components, the encoder cangenerate a list of candidate intra prediction modes. While HEVC had 35possible intra prediction modes for luma components, in JVET there are67 possible intra prediction modes for luma components. These include aplanar mode that uses a three-dimensional plane of values generated fromneighboring pixels, a DC mode that uses values averaged from neighboringpixels, and the 65 directional modes shown in FIG. 5 that use valuescopied from neighboring pixels along the indicated directions.

When generating a list of candidate intra prediction modes for a CU'sluma block component, the number of candidate modes on the list candepend on the CU's size. The candidate list can include: a subset ofHEVC's 35 modes with the lowest SATD (Sum of Absolute TransformDifference) costs; new directional modes added for JVET that neighborthe candidates found from the HEVC modes; and modes from a set of sixmost probable modes (MPMs) for the CU 102 that are identified based onintra prediction modes used for previously coded neighboring blocks aswell as a list of default modes.

When coding a CU's chroma block components, a list of candidate intraprediction modes can also be generated. The list of candidate modes caninclude modes generated with cross-component linear model projectionfrom luma samples, intra prediction modes found for luma CBs inparticular collocated positions in the chroma block, and chromaprediction modes previously found for neighboring blocks. The encodercan find the candidate modes on the lists with the lowest ratedistortion costs, and use those intra prediction modes when coding theCU's luma and chroma components. Syntax can be coded in the bitstreamthat indicates the intra prediction modes used to code each CU 102.

After the best intra prediction modes for a CU 102 have been selected,the encoder can generate a prediction CU 402 using those modes. When theselected modes are directional modes, a 4-tap filter can be used toimprove the directional accuracy. Columns or rows at the top or leftside of the prediction block can be adjusted with boundary predictionfilters, such as 2-tap or 3-tap filters.

The prediction CU 402 can be smoothed further with a position dependentintra prediction combination (PDPC) process that adjusts a prediction CU402 generated based on filtered samples of neighboring blocks usingunfiltered samples of neighboring blocks, or adaptive reference samplesmoothing using 3-tap or 5-tap low pass filters to process referencesamples.

When a CU 102 is coded temporally with inter prediction at 406, a set ofmotion vectors (MVs) can be found that points to samples in referencepictures that best predict pixel values of the CU 102. Inter predictionexploits temporal redundancy between slices by representing adisplacement of a block of pixels in a slice. The displacement isdetermined according to the value of pixels in previous or followingslices through a process called motion compensation. Motion vectors andassociated reference indices that indicate pixel displacement relativeto a particular reference picture can be provided in the bitstream to adecoder, along with the residual between the original pixels and themotion compensated pixels. The decoder can use the residual and signaledmotion vectors and reference indices to reconstruct a block of pixels ina reconstructed slice.

In JVET, motion vector accuracy can be stored at 1/16 pel, and thedifference between a motion vector and a CU's predicted motion vectorcan be coded with either quarter-pel resolution or integer-pelresolution.

In JVET motion vectors can be found for multiple sub-CUs within a CU102, using techniques such as advanced temporal motion vector prediction(ATMVP), spatial-temporal motion vector prediction (STMVP), affinemotion compensation prediction, pattern matched motion vector derivation(PMMVD), and/or bi-directional optical flow (BIO).

Using ATMVP, the encoder can find a temporal vector for the CU 102 thatpoints to a corresponding block in a reference picture. The temporalvector can be found based on motion vectors and reference pictures foundfor previously coded neighboring CUs 102. Using the reference blockpointed to by a temporal vector for the entire CU 102, a motion vectorcan be found for each sub-CU within the CU 102.

STMVP can find motion vectors for sub-CUs by scaling and averagingmotion vectors found for neighboring blocks previously coded with interprediction, together with a temporal vector.

Affine motion compensation prediction can be used to predict a field ofmotion vectors for each sub-CU in a block, based on two control motionvectors found for the top corners of the block. For example, motionvectors for sub-CUs can be derived based on top corner motion vectorsfound for each 4×4 block within the CU 102.

PMMVD can find an initial motion vector for the current CU 102 usingbilateral matching or template matching. Bilateral matching can look atthe current CU 102 and reference blocks in two different referencepictures along a motion trajectory, while template matching can look atcorresponding blocks in the current CU 102 and a reference pictureidentified by a template. The initial motion vector found for the CU 102can then be refined individually for each sub-CU.

BIO can be used when inter prediction is performed with bi-predictionbased on earlier and later reference pictures, and allows motion vectorsto be found for sub-CUs based on the gradient of the difference betweenthe two reference pictures.

In some situations, local illumination compensation (LIC) can be used atthe CU level to find values for a scaling factor parameter and an offsetparameter, based on samples neighboring the current CU 102 andcorresponding samples neighboring a reference block identified by acandidate motion vector. In JVET, the LIC parameters can change and besignaled at the CU level.

For some of the above methods the motion vectors found for each of aCU's sub-CUs can be signaled to decoders at the CU level. For othermethods, such as PMMVD and BIO, motion information is not signaled inthe bitstream to save overhead, and decoders can derive the motionvectors through the same processes.

After the motion vectors for a CU 102 have been found, the encoder cangenerate a prediction CU 402 using those motion vectors. In some cases,when motion vectors have been found for individual sub-CUs, OverlappedBlock Motion Compensation (OBMC) can be used when generating aprediction CU 402 by combining those motion vectors with motion vectorspreviously found for one or more neighboring sub-CUs.

When bi-prediction is used, JVET can use decoder-side motion vectorrefinement (DMVR) to find motion vectors. DMVR allows a motion vector tobe found based on two motion vectors found for bi-prediction using abilateral template matching process. In DMVR, a weighted combination ofprediction CUs 402 generated with each of the two motion vectors can befound, and the two motion vectors can be refined by replacing them withnew motion vectors that best point to the combined prediction CU 402.The two refined motion vectors can be used to generate the finalprediction CU 402.

At 408, once a prediction CU 402 has been found with intra prediction at404 or inter prediction at 406 as described above, the encoder cansubtract the prediction CU 402 from the current CU 102 find a residualCU 410.

The encoder can use one or more transform operations at 412 to convertthe residual CU 410 into transform coefficients 414 that express theresidual CU 410 in a transform domain, such as using a discrete cosineblock transform (DCT-transform) to convert data into the transformdomain. JVET allows more types of transform operations than HEVC,including DCT-II, DST-VII, DST-VII, DCT-VIII, DST-I, and DCT-Voperations. The allowed transform operations can be grouped intosub-sets, and an indication of which sub-sets and which specificoperations in those sub-sets were used can be signaled by the encoder.In some cases, large block-size transforms can be used to zero out highfrequency transform coefficients in CUs 102 larger than a certain size,such that only lower-frequency transform coefficients are maintained forthose CUs 102.

In some cases, a mode dependent non-separable secondary transform(MDNSST) can be applied to low frequency transform coefficients 414after a forward core transform. The MDNSST operation can use aHypercube-Givens Transform (HyGT) based on rotation data. When used, anindex value identifying a particular MDNSST operation can be signaled bythe encoder.

At 416, the encoder can quantize the transform coefficients 414 intoquantized transform coefficients 416. The quantization of eachcoefficient may be computed by dividing a value of the coefficient by aquantization step, which is derived from a quantization parameter (QP).In some embodiments, the Qstep is defined as 2(QP−4)/6. Because highprecision transform coefficients 414 can be converted into quantizedtransform coefficients 416 with a finite number of possible values,quantization can assist with data compression. Thus, quantization of thetransform coefficients may limit a number of bits generated and sent bythe transformation process. However, while quantization is a lossyoperation, and the loss by quantization cannot be recovered, thequantization process presents a trade-off between quality of thereconstructed sequence and an amount of information needed to representthe sequence. For example, a lower QP value can result in better qualitydecoded video, although a higher amount of data may be required forrepresentation and transmission. In contrast, a high QP value can resultin lower quality reconstructed video sequences but with lower data andbandwidth needs.

JVET may utilize variance-based adaptive quantization techniques, whichallows every CU 102 to use a different quantization parameter for itscoding process (instead of using the same frame QP in the coding ofevery CU 102 of the frame). The variance-based adaptive quantizationtechniques adaptively lowers the quantization parameter of certainblocks while increasing it in others. To select a specific QP for a CU102, the CU's variance is computed. In brief, if a CU's variance ishigher than the average variance of the frame, a higher QP than theframe's QP may be set for the CU 102. If the CU 102 presents a lowervariance than the average variance of the frame, a lower QP may beassigned.

At 420, the encoder can find final compression bits 422 by entropycoding the quantized transform coefficients 418. Entropy coding aims toremove statistical redundancies of the information to be transmitted. InJVET, CABAC (Context Adaptive Binary Arithmetic Coding) can be used tocode the quantized transform coefficients 418, which uses probabilitymeasures to remove the statistical redundancies. For CUs 102 withnon-zero quantized transform coefficients 418, the quantized transformcoefficients 418 can be converted into binary. Each bit (“bin”) of thebinary representation can then be encoded using a context model. A CU102 can be broken up into three regions, each with its own set ofcontext models to use for pixels within that region.

Multiple scan passes can be performed to encode the bins. During passesto encode the first three bins (bin0, bin1, and bin2), an index valuethat indicates which context model to use for the bin can be found byfinding the sum of that bin position in up to five previously codedneighboring quantized transform coefficients 418 identified by atemplate.

A context model can be based on probabilities of a bin's value being ‘0’or ‘1’. As values are coded, the probabilities in the context model canbe updated based on the actual number of ‘0’ and ‘1’ values encountered.While HEVC used fixed tables to re-initialize context models for eachnew picture, in WET the probabilities of context models for newinter-predicted pictures can be initialized based on context modelsdeveloped for previously coded inter-predicted pictures.

The encoder can produce a bitstream that contains entropy encoded bits422 of residual CUs 410, prediction information such as selected intraprediction modes or motion vectors, indicators of how the CUs 102 werepartitioned from a CTU 100 according to the QTBT structure, and/or otherinformation about the encoded video. The bitstream can be decoded by adecoder as discussed below. In some embodiments, the encoder can saveoverhead in the bitstream by omitting information from the bitstreamthat indicates which intra prediction modes were used to encode CUs 102,and the decoder can use template matching when decoding CUs 102 encodedwith intra prediction.

In addition to using the quantized transform coefficients 418 to findthe final compression bits 422, the encoder can also use the quantizedtransform coefficients 418 to generate reconstructed CUs 434 byfollowing the same decoding process that a decoder would use to generatereconstructed CUs 434. Thus, once the transformation coefficients havebeen computed and quantized by the encoder, the quantized transformcoefficients 418 may be transmitted to the decoding loop in the encoder.After quantization of a CU's transform coefficients, a decoding loopallows the encoder to generate a reconstructed CU 434 identical to theone the decoder generates in the decoding process. Accordingly, theencoder can use the same reconstructed CUs 434 that a decoder would usefor neighboring CUs 102 or reference pictures when performing intraprediction or inter prediction for a new CU 102. Reconstructed CUs 102,reconstructed slices, or full reconstructed images or frames may serveas references for further prediction stages.

At the encoder's decoding loop (and see below, for the same operationsin the decoder) to obtain pixel values for the reconstructed image, adequantization process may be performed. To dequantize a frame, forexample, a quantized value for each pixel of a frame is multiplied bythe quantization step, e.g., (Qstep) described above, to obtainreconstructed dequantized transform coefficients 426. For example, inthe decoding process shown in FIG. 4 in the encoder, the quantizedtransform coefficients 418 of a residual CU 410 can be dequantized at424 to find dequantized transform coefficients 426. If an MDNSSToperation was performed during encoding, that operation can be reversedafter dequantization.

At 428, the dequantized transform coefficients 426 can be inversetransformed to find a reconstructed residual CU 430, such as by applyinga DCT to the values to obtain the reconstructed image. At 432 thereconstructed residual CU 430 can be added to a corresponding predictionCU 402 found with intra prediction at 404 or inter prediction at 406, inorder to find a reconstructed CU 434. While in some embodiments theencoder can perform intra prediction at 404 as described above, in otherembodiments the encoder can perform intra prediction template matchingto generate a prediction CU 402 in the same way that a decoder would usetemplate matching for intra prediction if information identifying theintra prediction mode used for the CU 102 is omitted from the bitstream.

At 436, one or more filters can be applied to the reconstructed dataduring the decoding process (in the encoder or, as described below, inthe decoder), at either a picture level or CU level. For example, theencoder can apply a deblocking filter, a sample adaptive offset (SAO)filter, and/or an adaptive loop filter (ALF). The encoder's decodingprocess may implement filters to estimate and transmit to a decoder theoptimal filter parameters that can address potential artifacts in thereconstructed image. Such improvements increase the objective andsubjective quality of the reconstructed video. In deblocking filtering,pixels near a sub-CU boundary may be modified, whereas in SAO, pixels ina CTU 100 may be modified using either an edge offset or band offsetclassification. JVET's ALF can use filters with circularly symmetricshapes for each 2×2 block. An indication of the size and identity of thefilter used for each 2×2 block can be signaled.

If reconstructed pictures are reference pictures, they can be stored ina reference buffer 438 for inter prediction of future CUs 102 at 406.

During the above steps, JVET allows a content adaptive clippingoperations to be used to adjust color values to fit between lower andupper clipping bounds. The clipping bounds can change for each slice,and parameters identifying the bounds can be signaled in the bitstream.

FIG. 6 depicts a simplified block diagram for CU coding in a JVETdecoder. A JVET decoder can receive a bitstream, or bits, 602 containinginformation about encoded video data. The encoded video data mayrepresent partitioned luma blocks and partitioned chroma blocks, wherethe chroma blocks may be partitioned and coded independently of the lumablocks. The decoder may determine the respective coding mode respectiveto the blocks within each CU 102.

The bitstream can indicate how CUs 102 of a picture were partitionedfrom a CTU 100 according to a QTBT structure. The decoder may determinethe tree structure as part of obtaining syntax elements from thebitstream. The tree structure may specify how the initial video block,such as a CTB, is partitioned into smaller video blocks, such as codingunits.

As described herein, for each respective non-leaf node of the treestructure at each depth level of the tree structure, there are varioussplitting patterns for the respective non-leave node. By way of anon-limiting example, the bitstream can signal how CUs 102 werepartitioned from each CTU 100 in a QTBT using quadtree partitioning,symmetric binary partitioning, and/or asymmetric binary partitioning.The video block corresponding to such respective non-leaf node may bepartitioned into video blocks corresponding to the child nodes of therespective non-leaf node.

The bitstream can also indicate prediction information for the CUs 102such as intra prediction modes or motion vectors. Bits 602 represententropy encoded residual CUs. In some embodiments, syntax can be codedin the bitstream that indicates the intra prediction modes used to codeeach CU 102. In some embodiments, the encoder can have omittedinformation in the bitstream about intra prediction modes used to encodesome or all CUs 102 coded using intra prediction, and as such thedecoder can use template matching for intra prediction.

At 604 the decoder can decode the entropy encoded bits 602 using the CABAC context models signaled in the bitstream by the encoder. The decodercan use parameters signaled by the encoder to update the context models'probabilities in the same way they were updated during encoding.

After reversing the entropy encoding at 604 to find quantized transformcoefficients 606, the decoder can dequantize them at 608 to finddequantized transform coefficients 610. If an MDNSST operation wasperformed during encoding, that operation can be reversed by the decoderafter dequantization.

At 612, the dequantized transform coefficients 610 can be inversetransformed to find a reconstructed residual CU 614. At 616, thereconstructed residual CU 614 can be added to a corresponding predictionCU 626 found with intra prediction at 622 or inter prediction at 624, inorder to find a reconstructed CU 618. In some embodiments, the decodercan find the prediction CU 626 using template matching for intraprediction.

At 620, one or more filters can be applied to the reconstructed data, ata picture level or CU level, for example. For example, the decoder canapply a deblocking filter, a sample adaptive offset (SAO) filter, and/oran adaptive loop filter (ALF). As described above, the in-loop filterslocated in the decoding loop of the encoder may be used to estimateoptimal filter parameters to increase the objective and subjectivequality of a frame. These parameters are transmitted to the decoder tofilter the reconstructed frame at 620 to match the filteredreconstructed frame in the encoder.

After reconstructed pictures have been generated by findingreconstructed CUs 618 and applying signaled filters, the decoder canoutput the reconstructed pictures as output video 628. If reconstructedpictures are to be used as reference pictures, they can be stored in areference buffer 630 for inter prediction of future CUs 102 at 624.

In some embodiments, the bitstream received by a JVET decoder caninclude syntax identifying which intra prediction mode was used toencode a CU 102 with intra prediction, such that the decoder candirectly use the signaled intra prediction mode at 622 to generate aprediction CU 626. In some embodiments, such syntax can be omitted tosave overhead by reducing the number of bits in the bitstream. In theseembodiments, when the decoder is not provided with an indication ofwhich intra prediction mode was used to encode a CU 102, the decoder canuse template matching for intra prediction at 622 to derive the intraprediction mode it should use to generate a prediction CU 626. In someembodiments, an encoder can similarly use template matching for intraprediction at 404 when generating a prediction CU 402 to combine with areconstructed residual CU 430 at 432 within its decoding loop.

As described herein, intra coding is a main tool for video compression.It utilizes the spatial neighbors of a pixel to create a predictor, fromwhich a prediction residual between the pixel and its predictor isdetermined. Video encoder then compresses the residuals, resulting inthe coding bitstream. The developing video coding standard, WET, allows67 possible intra prediction modes, including planar mode, DC mode, and65 angular direction modes, as shown in FIG. 5. Each intra predictionmode has unique prediction generation method, based on either left-sideneighbor or top-side neighbor. Each intra coding unit (CU) selects atleast one intra prediction mode to be used, which needs to be signaledas overhead in bitstream. For example, where a single CU includes oneluma coding block and two chroma coding blocks, each of the luma codingblock and chroma coding block can have their own intra prediction mode.Thus, if signaled in the bitstream, multiple intra prediction modes maybe identified.

Disclosed herein are various techniques to implement an unequal weightplanar prediction mode (UW-Planar). In embodiments, the prediction modeapproximates a predictor and prediction residuals that generatespredicted pixels that are closer to the actual pixels, resulting inimproved coding efficiency.

Planar mode is often the most frequently used intra coding mode in HEVCand JVET. Planar mode is often suited for blocks with a smooth imagewhose pixel values gradually change with a small planar gradient. FIGS.7A and 7B show the HEVC and JVET planar predictor generation process fora coding unit (block) 702 with height H=8 and width W=8, where the (0,0)coordinates corresponds to the top-left (TL) position 704 within thecoding CU, where TL position 718 is a top left neighboring corner pixel.TR 706 denotes top right position and BL 708 denotes bottom leftposition. The dashed line 710 indicates interpolation and the dottedline 712 indicates replication. FIG. 7A depicts the horizontal predictorcalculation and FIG. 7B depicts a vertical predictor calculation.

Planar mode in HEVC and JVET (HEVC Planar) generates a first orderapproximation of the prediction for a current coding unit (CU) byforming a plane based on the intensity values of the neighboring pixels.Due to a raster-scan coding order, the reconstructed left columnneighboring pixels 714 and the reconstructed top row neighboring pixels716 are available for a current coding block 702, but not the rightcolumn neighboring pixels and the bottom row neighboring pixels. Theplanar predictor generation process sets the intensity values of all theright column neighboring pixels to be the same as the intensity value ofthe top right neighboring pixel 706, and the intensity values of all thebottom row pixels to be the same as the intensity value of the bottomleft neighboring pixel 708.

Once the neighboring pixels surrounding a current coding block aredefined, the horizontal and the vertical predictors (P_(h)(x, y) andP_(v)(x, y), respectively) for each pixel within the current codingblock 702 are determined according to equations (1) and (2) below.P _(h)(x,y)=(W−1−x)*R(−1,+(x+1)*R(W,−1)  (1)P _(v)(x,y)=(H−1−y)*R(x,−1)+(y+1)*R(−1,H)  (2)

-   -   Where:    -   R(x, y) denotes the intensity value of the reconstructed        neighboring pixel at (x, y) coordinate,    -   W is block width, and    -   H is block height.

As depicted in FIG. 7A, a horizontal predictor is interpolated fromreferencing reconstructed reference sample Ly 722, a pixel in the leftcolumn neighboring pixels 714, that is at the same y-coordinate as thecurrent sample C 724, and referencing sample Ty 726 which is a copy ofpreviously reconstructed sample TR 718 that is adjacent to the top-rightpixel of the coding unit. Thus, the horizontal predictor is calculatedusing linear interpolation between a value of a respective horizontalboundary pixel and a value of a vertical boundary pixel.

As depicted in FIG. 7B, a vertical predictor is interpolated fromreconstructed sample Tx 728 from the top row neighboring pixels at thesame x-coordinate as the current prediction pixel C and referencingsample Lx 730 which is a copy of the previously reconstructed sample BL708, a neighboring left pixel that is adjacent to the bottom left sampleof the coding unit. Thus, the vertical predictor is calculated usingliner interpolation between a value of a respective vertical boundarypixel and a value of a horizontal boundary value.

Once the horizontal and vertical predictor is determined for a codingunit pixel, the final predictor can be determined. For each pixel in acoding block, the final planar predictor, P(x, y), is the intensityvalue that may be computed by averaging the horizontal and verticalpredictors according to equation (3), with certain adjustments inembodiments in which the current CU or coding block is non-square.P(x,y)=(H*P _(h)(x,y)+W*P _(v)(x,y))/(2*H*W)  (3)

The encoder may also signal a residual between the prediction codingunit and the current coding unit in the bitstream to the decoder.

Certain constraints with the current planar mode in JVET may causecoding inefficiencies. For example, as described above, all the rightcolumn neighboring pixels are set to the same intensity value, which isthe value of the top right neighboring pixel. Similarly, all the bottomrow neighboring pixels are set to the same intensity value, which is thevalue of the bottom left neighboring pixel. Setting all right columnpixels the same, and all bottom row pixels the same, conflicts withplane prediction assumption. Thus, these constraints cause the planarprediction block to deviate from an ideal flat plane. FIG. 8 depicts anexample of planar prediction plane with the JVET method that deviatesfrom the flat plane (TR denotes top right position and BL denotes bottomleft position).

Another constraint of the current JVET planar mode comes from the finalpredictor computation of equation (3), where equal weights are assignedto both the horizontal and the vertical predictors. Equal weightassignment may be suboptimal, given the current planar generationprocess setup. Specifically, the top row and the left column neighboringpixels are the reconstructed pixels, and they are more accurate comparedto the corresponding original or actual top row and left column pixelsthan the derived bottom row and right column neighboring pixels are tothe original or actual bottom row and right column pixels, respectively.The bottom row and right column neighboring pixels are non-causal andtheir intensity values are estimated. Thus, depending on a pixelposition within a CU, the horizontal and/or vertical predictor accuracyvaries based on the proximity to a more reliable neighboring pixel usedin the calculation of the predictor.

Disclosed herein are embodiments for deriving an intensity value of abottom right neighboring pixel P(W,H) for a current coding block. Inembodiments, a coding unit may be the smallest coding block(s) within aCTU that is coded. In embodiments, a coding unit may include luma andchroma coding blocks that are independently coded. The proposedtechniques include computing an intensity value of the bottom rightneighboring pixel (lifting), and then computing the intensity values ofthe bottom row and the right column neighboring pixels, using thederived intensity value of the bottom right neighboring pixel along withthe intensity values of other corner neighboring pixels, such as the topright neighboring pixel and the bottom left neighboring pixel. It isnoted that a corner pixel at coordinates x=0, y=0 within the codingblock is different from the corner neighboring pixels (e.g., P(W,H))which is outside the coding block.

An example of a lifting process is a weighted average of the top rightand the bottom left neighboring pixels, as defined in equation (4).P(W,H)=(W*R(W,−1)+H*R(−1,H))/(H+W)  (4)

Another example of lifting process is maintaining a flat plane based onthe intensity value of the top left, the top right, and the bottom leftneighboring pixels, as defined in equation (5).P(W,H)=R(W,−1)+R(−1,H)−R(−1,−1)  (5)

FIG. 9 illustrates the planar prediction block generated based onequation (5), with the corner intensity values identical to an examplein FIG. 8. As shown, a flat planar prediction plane results from usingequation (5) given the same three corner points as in FIG. 8 (TR denotestop-right position, TL denotes top-left position and BL denotesbottom-left position).

With the derived intensity value of the bottom right neighboring pixelof P(W, H), the intensity values of the bottom row neighboring pixels,P_(b)(x, H), and the right column neighboring pixels, P_(r)(W, y), canbe computed accordingly. One example of such computation follows linearinterpolation according to equations (6) and (7).

$\begin{matrix}{{P_{b}\left( {x,H} \right)} = \frac{{\left( {W - 1 - x} \right)*{R\left( {{- 1},H} \right)}} + {\left( {x + 1} \right)*{P\left( {W,H} \right)}}}{W}} & (6) \\{{P_{r}\left( {W,y} \right)} = \frac{{\left( {H - 1 - y} \right)*{R\left( {W,{- 1}} \right)}} + {\left( {y + 1} \right)*{P\left( {W,H} \right)}}}{H}} & (7)\end{matrix}$

Once the neighboring pixels surrounding a current CU are defined, thehorizontal and the vertical predictors (P_(h)(x, y) and P_(v)(x, y),respectively) for each pixel within the CU are determined according toequations (8) and (9) below.P _(h)(x,y)=(W−1−x)*R(−1,y)+(x+1)*P _(r)(W,y)  (8)P _(v)(x,y)=(H−1−y)*R(x,−1)+(y+1)*P _(b)(x,H)  (9)

Notice that in equations (8) and (9), P_(h)(x, y) and P_(v)(x, y) areshown as scaled up version of horizontal and vertical predictors. Thesefactors will be compensated in the final predictor calculation step.

It may also be beneficial to use filtered version of neighboring pixelsintensity, instead of actual neighboring pixel intensity for liftingpart of the bottom right position adjustment. This is especially truewhen the coding content is noisy. An example of such filtering operationis described in equations (10) and (11).

$\begin{matrix}{{R^{\prime}\left( {{- 1},H} \right)} = \frac{\begin{pmatrix}{{R\left( {{- 1},{H - 3}} \right)} + {3*R\left( {{- 1},{H - 2}} \right)} +} \\{{5*R\left( {{- 1},{H - 1}} \right)} + {7*{R\left( {{- 1},H} \right)}}}\end{pmatrix}}{16}} & (10) \\{{R^{\prime}\left( {W,{- 1}} \right)} = \frac{\begin{pmatrix}{{R\left( {{W - 3},{- 1}} \right)} + {3*{R\left( {{W - 2},{- 1}} \right)}} +} \\{{5*{R\left( {{W - 1},{- 1}} \right)}} + {7*{R\left( {W,{- 1}} \right)}}}\end{pmatrix}}{16}} & (11)\end{matrix}$

Another example of filtering operation is described in equations (10a)and (11a)

$\begin{matrix}{{R^{\prime}\left( {{- 1},H} \right)} = \frac{\left( {{R\left( {{- 1},{H - 2}} \right)} + {2*{R\left( {{- 1},{H - 1}} \right)}} + {5*{R\left( {{- 1},H} \right)}}} \right)}{8}} & \left( {10a} \right) \\{{R^{\prime}\left( {W,{- 1}} \right)} = \frac{\left( {{R\left( {{W - 2},{- 1}} \right)} + {2*{R\left( {{W - 1},{- 1}} \right)}} + {5*{R\left( {W,{- 1}} \right)}}} \right)}{8}} & \left( {11a} \right)\end{matrix}$

The filtering operation may be selectively employed based on whetherneighboring pixels have already been filtered by other prior process(such as mode dependent intra smoothing in HEVC). In embodiments, whenneighboring pixels have not been filtered by prior task, the filteringoperation is used. Otherwise, the filtering operation is not used.

Alternatively, it may be beneficial to set top-right and bottom-leftcorner positions to be R(W−1, −1) and R(−1, H−1), respectively. In thissituation, interpolation for intermediate predictors can be described asan example by equations (12)-(16).

$\begin{matrix}{{P\left( {{W - 1},{H - 1}} \right)} = {\left( {{W*{R\left( {{W - 1},{- 1}} \right)}} + {H*{R\left( {{- 1},{H - 1}} \right)}}} \right)/\left( {H + W} \right)}} & (12) \\{{P_{b}\left( {x,{H - 1}} \right)} = \frac{{\left( {W - 1 - x} \right)*{R\left( {{- 1},{H - 1}} \right)}} + {\left( {x + 1} \right)*{P\left( {{W - 1},{H - 1}} \right)}}}{W}} & (13) \\{{P_{r}\left( {{W - 1},y} \right)} = \frac{{\left( {H - 1 - y} \right)*{R\left( {{W - 1},{- 1}} \right)}} + {\left( {y + 1} \right)*{P\left( {{W - 1},{H - 1}} \right)}}}{H}} & (14) \\{\mspace{79mu}{{P_{h}\left( {x,y} \right)} = {{\left( {W - 1 - x} \right)*{R\left( {{- 1},y} \right)}} + {\left( {x + 1} \right)*{P_{r}\left( {{W - 1},y} \right)}}}}} & (15) \\{\mspace{79mu}{{P_{v}\left( {x,y} \right)} = {{\left( {H - 1 - y} \right)*{R\left( {x,{- 1}} \right)}} + {\left( {y + 1} \right)*{P_{b}\left( {x,{H - 1}} \right)}}}}} & (16)\end{matrix}$

It is further proposed to utilize unequal weights for the final planarpredictor calculation. In embodiments in which unequal weight isemployed, an improvement may result from the accuracy of the inputintensity in the final interpolation process. For example, inembodiments, larger weights are applied to the positions that are closerto more reliable neighboring positions.

In JVET, the processing order follows raster scan at CTU level andz-scan for CU within a CTU. Hence, the top row and the left columnneighboring pixels are the actual reconstructed pixels and hence aremore reliable than the bottom row and right column neighboring pixels(which are estimated ones). An example of unequal weight employed at thefinal predictor calculation is described in equation (17).P(x,=((H*P _(h)(x,y)*(y+1)+W*P _(v)(x,y)*(x+1)))/((H*W*(x+y+2)))   (17)

An example of unequal weight assignment shown in equation (17) can begeneralized into a generic equation as shown in equation (18).P(x,y)=(A(x,y)*P _(h)(x,y)+B(x,y)*P _(v)(x+c(x,y))/(D(x,y))  (18)

-   -   Where:    -   A(x, y) and B(x, y) are the position dependent weighting factors        for horizontal and vertical predictors, respectively,    -   c(x, y) is a position dependent rounding factor, and    -   D(x, y) is a position dependent scaling factor.

Thus, unlike weights assigned to the horizontal and vertical predictorsthat are equal in the final predictor computation step, unequal weightsare assigned to the horizontal and vertical predictors. In embodiments,the unequal weights assigned to the horizontal and vertical predictorsis assigned for each predictor pixel within the coding unit. Inembodiments, the unequal weights are assigned according to a distanceratio from a reliable neighboring pixel for each of the horizontalpredictor and vertical predictor. In embodiments, the distance ratio isan approximation based on Euclidean distance using an “ordinary”straight-line distance between two points in Euclidean space,embodiments further including rounding.

Note that unequal weight assignment can also be used at the horizontaland vertical predictor computation phase as well. The bottom rightposition adjustment and unequal weight assignment components of thetechniques disclosed herein can be used together or separately dependingon codec design consideration.

For optimal coding performance, weighting factors and lifting processcan be modified according to picture type (I, P, B, etc.), temporallayer, color component (Y, Cb, Cr, etc.). It is also possible to employboth weighted Planar prediction and HEVC Planar prediction for certainsituation within a sequence; e.g., weighted Planar for I slice and HEVCPlanar for P and B slices.

Many equations, e.g., (4), (6), (7) and (17), for the bottom rightposition adjustment process and unequal weight assignment process,respectively, involve division operations, which can be costly in termsof complexity. These division operations can be roughly converted intoscale operations to make them implementation friendly, as described inequations (19), (20), (21) and (22).P(W,H)=((W*R(W,−1)+H*R(−1,H))*S[W+H])>>ShiftDenom  (19)P _(b)(x,H)=(((W−1−x)*R(−1,H)+(x+1)*P(W,H))*S[W])>>ShiftDenom  (20)P _(r)(W,y)=(((H−1−y)*R(W,−1)+(y+1)*P(W,H))*S[H])>>ShiftDenom  (21)P(x,y)=((H*P _(h)(x,y)*(y+1)W*P_(v)(x,y)*(x+1))*S[x+y+2])>>(ShiftDenom+log₂ W+log₂ H)  (22)

-   -   Where:    -   S[n] is a weight of parameter n, and    -   >>denotes a bit shift operation to the right    -   ShiftDenom is a factor for shifted down operation.

Specifically, S[n] may be an approximation of a factor

$\frac{1}{n},$and can be described as shown in equation (4).

$\begin{matrix}{{S\lbrack n\rbrack} = {{Round}\left( \frac{\left( {1 ⪡ {ShiftDenom}} \right)}{n} \right)}} & (4)\end{matrix}$

FIG. 10 is an example of S[n], where sum of width and height is 256 andShiftDenom=10.

FIG. 11 illustrates another example of S[n], where the sum of the widthand height is 512 and ShiftDenom=10.

In the two examples above, memory size of 2570 bits (257 entries with 10bits each) and 5130 bits (513 entries with 10 bits each) are required tohold the weight tables. This memory size may be excessive and it may bebeneficial to reduce this memory requirement. Two examples below are twopossible ways to accomplish this requirement.

FIG. 12 illustrates another example of S[n], where sum of width andheight is 128 and ShiftDenom=10.

FIG. 13 illustrates another example of S[n], where sum of width andheight is 128 and ShiftDenom=9.

In the third and fourth examples, only 126 entries of 129 necessary arerequired to stored, since the first two entries (1/0 and 1/1) are notused in the proposed method. The third entry, representing ½, has valueof 512 and 256 in the third and fourth examples, respectively, and it ishandled separately during weight calculation. Weight averagecalculations shown in equations and 21 will be modified as shown inequations 24 and 25 below.P _(b)(x,H)=(((W−1−x)*R(−1,H)+(x+1)*P(W,H))*S[W−3])>>ShiftDenom   (24)P _(r)(W,y)=(((H−1−y)*R(W,−1)+(y+1)*P(W,H))*S[H−3])>>ShiftDenom   (25)

Simple shift conversion used in Equation (22) does not provide accurateoutputs resulting poor coding efficiency. The ineffectiveness is due toconversion process which allows error to accumulate linearly withdistance. One way to reduce this error is to exploit the fact thatweight for horizontal and vertical predictors are complimentary in (17)and hence the real weight can be computed based on that of horizontal orvertical predictor, whichever is more accurate.

An example of this approach is now described. First, parametershorWeight and verWeight are introduced and (22) can now be described as(26).

$\begin{matrix}{{P\left( {x,y} \right)} = {\left( \left( {{H*{P_{h}\left( {x,y} \right)}*{horWeight}} + {W*{P_{v}\left( {x,y} \right)}*{verWeight}}} \right) \right) ⪢ \left( {{ShiftDenom} + {\log_{2}W} + {\log_{2}H}} \right)}} & (26) \\{\mspace{79mu}{{horWeight} = \left\{ \begin{matrix}{\left( {1 ⪡ {ShiftDenom}} \right) - {verWeight}} & {{{when}\mspace{14mu} x} < y} \\{\left( {y + 1} \right)*{S\left\lbrack {x + y + 2} \right\rbrack}} & {otherwise}\end{matrix} \right.}} & (27) \\{\mspace{79mu}{{verWeight} = \left\{ \begin{matrix}{\left( {x + 1} \right)*{S\left\lbrack {x + y + 2} \right\rbrack}} & {{{when}\mspace{14mu} x} < y} \\{\left( {1 ⪡ {ShiftDenom}} \right) - {horWeight}} & {otherwise}\end{matrix} \right.}} & (28)\end{matrix}$

Similar example is also given to handle weight tables given in examples3 and 4.

$\begin{matrix}{{P\left( {x,y} \right)} = {\left( \left( {{H*{P_{h}\left( {x,y} \right)}*{horWeight}} + {W*{P_{v}\left( {x,y} \right)}*{verWeight}}} \right) \right) ⪢ \left( {{ShiftDenom} + {\log_{2}W} + {\log_{2}H}} \right)}} & (26) \\{\mspace{79mu}{{horWeight} = \left\{ \begin{matrix}{\left( {1 ⪡ {ShiftDenom}} \right) - {verWeight}} & {{{when}\mspace{14mu} x} < y} \\{\left( {y + 1} \right)*{S\left\lbrack {x + y - 1} \right\rbrack}} & {{{when}\mspace{14mu} x} \geq y}\end{matrix} \right.}} & (29) \\{\mspace{79mu}{{verWeight} = \left\{ \begin{matrix}{\left( {x + 1} \right)*{S\left\lbrack {x + y - 1} \right\rbrack}} & {{{when}\mspace{14mu} x} < y} \\{\left( {1 ⪡ {ShiftDenom}} \right) - {horWeight}} & {otherwise}\end{matrix} \right.}} & (30) \\{{horWeight} = {{verWeight} = \left( {{1 ⪡ {\left( {{ShiftDenom} - 1} \right)\mspace{14mu}{when}\mspace{14mu} x}} = {y = 0}} \right.}} & (31)\end{matrix}$

According to (6) and (7) above, the bottom row and right columnneighboring lines of a current block are formed. The pixel values ofthese two lines are further used to compute the prediction of currentblock. Ideally, the pixels in these two lines are more reliable whenthey are close to R(W, −1) and R(−1, H) while those pixels close to thebottom right neighboring pixel of P(W, H) is less reliable.

The execution of the sequences of instructions required to practice theembodiments can be performed by a computer system 1400 as shown in FIG.14. In an embodiment, execution of the sequences of instructions isperformed by a single computer system 1400. According to otherembodiments, two or more computer systems 1400 coupled by acommunication link 1415 can perform the sequence of instructions incoordination with one another. Although a description of only onecomputer system 1400 will be presented below, however, it should beunderstood that any number of computer systems 1400 can be employed topractice the embodiments.

A computer system 1400 according to an embodiment will now be describedwith reference to FIG. 14, which is a block diagram of the functionalcomponents of a computer system 1400. As used herein, the term computersystem 1400 is broadly used to describe any computing device that canstore and independently run one or more programs.

Each computer system 1400 can include a communication interface 1414coupled to the bus 1406. The communication interface 1414 providestwo-way communication between computer systems 1400. The communicationinterface 1414 of a respective computer system 1400 transmits andreceives electrical, electromagnetic or optical signals, which includedata streams representing various types of signal information, e.g.,instructions, messages and data. A communication link 1415 links onecomputer system 1400 with another computer system 1400. For example, thecommunication link 1415 can be a LAN, in which case the communicationinterface 1414 can be a LAN card, or the communication link 1415 can bea PSTN, in which case the communication interface 1414 can be anintegrated services digital network (ISDN) card or a modem, or thecommunication link 1415 can be the Internet, in which case thecommunication interface 1414 can be a dial-up, cable or wireless modem.

A computer system 1400 can transmit and receive messages, data, andinstructions, including program, i.e., application, code, through itsrespective communication link 1415 and communication interface 1414.Received program code can be executed by the respective processor(s)1407 as it is received, and/or stored in the storage device 1410, orother associated non-volatile media, for later execution.

In an embodiment, the computer system 1400 operates in conjunction witha data storage system 1431, e.g., a data storage system 1431 thatcontains a database 1432 that is readily accessible by the computersystem 1400. The computer system 1400 communicates with the data storagesystem 1431 through a data interface 1433. A data interface 1433, whichis coupled to the bus 1406, transmits and receives electrical,electromagnetic or optical signals, which include data streamsrepresenting various types of signal information, e.g., instructions,messages and data. In embodiments, the functions of the data interface1433 can be performed by the communication interface 1414.

Computer system 1400 includes a bus 1406 or other communicationmechanism for communicating instructions, messages and data,collectively, information, and one or more processors 1407 coupled withthe bus 1406 for processing information. Computer system 1400 alsoincludes a main memory 1408, such as a random-access memory (RAM) orother dynamic storage device, coupled to the bus 1406 for storingdynamic data and instructions to be executed by the processor(s) 1407.The main memory 1408 also can be used for storing temporary data, i.e.,variables, or other intermediate information during execution ofinstructions by the processor(s) 1407.

The computer system 1400 can further include a read only memory (ROM)1409 or other static storage device coupled to the bus 1406 for storingstatic data and instructions for the processor(s) 1407. A storage device1410, such as a magnetic disk or optical disk, can also be provided andcoupled to the bus 1406 for storing data and instructions for theprocessor(s) 1407.

A computer system 1400 can be coupled via the bus 1406 to a displaydevice 1411, such as, but not limited to, a cathode ray tube (CRT) or aliquid-crystal display (LCD) monitor, for displaying information to auser. An input device 1412, e.g., alphanumeric and other keys, iscoupled to the bus 1406 for communicating information and commandselections to the processor(s) 1407.

According to one embodiment, an individual computer system 1400 performsspecific operations by their respective processor(s) 1407 executing oneor more sequences of one or more instructions contained in the mainmemory 1408. Such instructions can be read into the main memory 1408from another computer-usable medium, such as the ROM 1409 or the storagedevice 1410. Execution of the sequences of instructions contained in themain memory 1408 causes the processor(s) 1407 to perform the processesdescribed herein. In alternative embodiments, hard-wired circuitry canbe used in place of or in combination with software instructions. Thus,embodiments are not limited to any specific combination of hardwarecircuitry and/or software.

The term “computer-usable medium,” as used herein, refers to any mediumthat provides information or is usable by the processor(s) 1407. Such amedium can take many forms, including, but not limited to, non-volatile,volatile and transmission media. Non-volatile media, i.e., media thatcan retain information in the absence of power, includes the ROM 1409,CD ROM, magnetic tape, and magnetic discs. Volatile media, i.e., mediathat cannot retain information in the absence of power, includes themain memory 1408. Transmission media includes coaxial cables, copperwire and fiber optics, including the wires that comprise the bus 1406.Transmission media can also take the form of carrier waves; i.e.,electromagnetic waves that can be modulated, as in frequency, amplitudeor phase, to transmit information signals. Additionally, transmissionmedia can take the form of acoustic or light waves, such as thosegenerated during radio wave and infrared data communications. In theforegoing specification, the embodiments have been described withreference to specific elements thereof. It will, however, be evidentthat various modifications and changes can be made thereto withoutdeparting from the broader spirit and scope of the embodiments. Forexample, the reader is to understand that the specific ordering andcombination of process actions shown in the process flow diagramsdescribed herein is merely illustrative, and that using different oradditional process actions, or a different combination or ordering ofprocess actions can be used to enact the embodiments. The specificationand drawings are, accordingly, to be regarded in an illustrative ratherthan restrictive sense.

It should also be noted that the present invention can be implemented ina variety of computer systems. The various techniques described hereincan be implemented in hardware or software, or a combination of both.Preferably, the techniques are implemented in computer programsexecuting on programmable computers that each include a processor, astorage medium readable by the processor (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device. Program code is applied to data enteredusing the input device to perform the functions described above and togenerate output information. The output information is applied to one ormore output devices. Each program is preferably implemented in ahigh-level procedural or object-oriented programming language tocommunicate with a computer system. However, the programs can beimplemented in assembly or machine language, if desired. In any case,the language can be a compiled or interpreted language. Each suchcomputer program is preferably stored on a storage medium or device(e.g., ROM or magnetic disk) that is readable by a general or specialpurpose programmable computer for configuring and operating the computerwhen the storage medium or device is read by the computer to perform theprocedures described above. The system can also be considered to beimplemented as a computer-readable storage medium, configured with acomputer program, where the storage medium so configured causes acomputer to operate in a specific and predefined manner.

FIG. 15A is a flow diagram that illustrates a method for performing thedisclosed techniques, but it should be understood that the techniquesdescribed herein with respect to the remaining figures similarly capturethe methods available using the disclosed techniques. As illustrated inFIG. 15A, the method includes calculating a final planar prediction inplanar mode to predict pixel values for a current coding block havingheight H, width W, and a top-left (TL) pixel within the current codingblock is defined by coordinates x=0 and y=0. At 1502, a decodingsoftware or hardware module receives a video bitstream with encodeddata. The method at 1504 comprises interpolating a horizontal predictorand a vertical predictor for a pixel in a current coding block receivedin a vide bitstream. At 1506, a final planar prediction value P(x,y) maybe calculated using unequal weights applied to each of the first andsecond predictors. The final planar prediction value may be determinedin accordance with P(x, y)=(A(x, y)*P_(h)(x, y)+B(x, y)*P_(v)(x, y)+c(x,y))/(D(x, y)), where A(x, y) and B(x, y) are position dependentweighting factors for the horizontal and vertical predictors,respectively, c(x, y) is a position dependent rounding factor, and D(x,y) is a position dependent scaling factor.

FIG. 15B depicts a flow diagram for using the unequal weight planarprediction described herein and illustrated in FIG. 15A using acalculated intensity value of a bottom right neighboring pixel of thecurrent coding block. As illustrated in FIG. 15B, following receipt of avideo bitstream with encoded data at 1502, the method may includecalculating at 1503 an intensity value of a bottom right neighboringpixel of the current coding block, such as a coding unit or a luma orchroma block. At 1504, the bottom right intensity value can be used witha neighboring pixel from the column of vertical boundary pixels on theleft side of the current coding block to calculate intensity values forthe neighboring pixels in a row along the lower side of the currentblock. At 1506, the bottom right intensity value can be used with anintensity value of a neighboring pixel from the row of horizontalboundary pixels on the upper side of the current coding block tocalculate intensity values for the neighboring pixels in a column alongthe right side of the current block. The method at 1508 and 1510 furthercomprises calculating a first and second predictor and, at 1511, a finalplanar prediction value P(x,y) may be calculated using unequal weightsapplied to each of the first and second predictors. The final planarprediction value may be determined in accordance with P(x, y)=(A(x,y)*P_(h)(x, +B(x, y)*P_(v)(x, y)+c(x, y))/(D(x, y)), where A(x, y) andB(x, y) are position dependent weighting factors for the horizontal andvertical predictors, respectively, c(x, y) is a position dependentrounding factor, and D(x, y) is a position dependent scaling factor. At1512, the prediction pixel values of the coding block may be predicted.

FIG. 16 is a high-level view of a source device 12 and destinationdevice 10 that may incorporate features of the systems and devicesdescribed herein. As shown in FIG. 16, example video coding system 10includes a source device 12 and a destination device 14 where, in thisexample, the source device 12 generates encoded video data. Accordingly,source device 12 may be referred to as a video encoding device.Destination device 14 may decode the encoded video data generated bysource device 12. Accordingly, destination device 14 may be referred toas a video decoding device. Source device 12 and destination device 14may be examples of video coding devices.

Destination device 14 may receive encoded video data from source device12 via a channel 16. Channel 16 may comprise a type of medium or devicecapable of, moving the encoded video data from source device 12 todestination device 14. In one example, channel 16 may comprise acommunication medium that enables source device 12 to transmit encodedvideo data directly to destination device 14 in real-time.

In this example, source device 12 may modulate the encoded video dataaccording to a communication standard, such as a wireless communicationprotocol, and may transmit the modulated video data to destinationdevice 14. The communication medium may comprise a wireless or wiredcommunication medium, such as a radio frequency (RF) spectrum or one ormore physical transmission lines. The communication medium may form partof a packet-based network, such as a local area network, a wide-areanetwork, or a global network such as the Internet. The communicationmedium may include routers, switches, base stations, or other equipmentthat facilitates communication from source device 12 to destinationdevice 14. In another example, channel 16 may correspond to a storagemedium that stores the encoded video data generated by source device 12.

In the example of FIG. 16, source device 12 includes a video source 18,video encoder 20, and an output interface 22. In some cases, outputinterface 28 may include a modulator/demodulator (modem) and/or atransmitter. In source device 12, video source 18 may include a sourcesuch as a video capture device, e.g., a video camera, a video archivecontaining previously captured video data, a video feed interface toreceive video data from a video content provider, and/or a computergraphics system for generating video data, or a combination of suchsources.

Video encoder 20 may encode the captured, pre-captured, orcomputer-generated video data. An input image may be received by thevideo encoder 20 and stored in the input frame memory 21. Thegeneral-purpose processor 23 may load information from here and performencoding. The program for driving the general-purpose processor may beloaded from a storage device, such as the example memory modulesdepicted in FIG. 16. The general-purpose processor may use processingmemory 22 to perform the encoding, and the output of the encodinginformation by the general processor may be stored in a buffer, such asoutput buffer 26.

The video encoder 20 may include a resampling module 25 which may beconfigured to code (e.g., encode) video data in a scalable video codingscheme that defines at least one base layer and at least one enhancementlayer. Resampling module 25 may resample at least some video data aspart of an encoding process, wherein resampling may be performed in anadaptive manner using resampling filters.

The encoded video data, e.g., a coded bit stream, may be transmitteddirectly to destination device 14 via output interface 28 of sourcedevice 12. In the example of FIG. 16, destination device 14 includes aninput interface 38, a video decoder 30, and a display device 32. In somecases, input interface 28 may include a receiver and/or a modem. Inputinterface 38 of destination device 14 receives encoded video data overchannel 16. The encoded video data may include a variety of syntaxelements generated by video encoder 20 that represent the video data.Such syntax elements may be included with the encoded video datatransmitted on a communication medium, stored on a storage medium, orstored a file server.

The encoded video data may also be stored onto a storage medium or afile server for later access by destination device 14 for decodingand/or playback. For example, the coded bitstream may be temporarilystored in the input buffer 31, then loaded in to the general-purposeprocessor 33. The program for driving the general-purpose processor maybe loaded from a storage device or memory. The general-purpose processormay use a process memory 32 to perform the decoding.

FIG. 16 depicts the resampling module 35 separately from thegeneral-purpose processor 33, but it would be appreciated by one ofskill in the art that the resampling function may be performed by aprogram executed by the general-purpose processor, and the processing inthe video encoder may be accomplished using one or more processors. Thedecoded image(s) may be stored in the output frame buffer 36 and thensent out to the input interface 38.

Display device 38 may be integrated with or may be external todestination device 14. In some examples, destination device 14 mayinclude an integrated display device and may also be configured tointerface with an external display device. In other examples,destination device 14 may be a display device. In general, displaydevice 38 displays the decoded video data to a user.

Video encoder 20 and video decoder 30 may operate according to a videocompression standard. ITU-T VCEG (Q6/16) and ISO/IEC MPEG (JTC 1/SC29/WG 11) are studying the potential need for standardization of futurevideo coding technology with a compression capability that significantlyexceeds that of the current High Efficiency Video Coding HEVC standard(including its current extensions and near-term extensions for screencontent coding and high-dynamic-range coding). The groups are workingtogether on this exploration activity in a joint collaboration effortknown as the Joint Video Exploration Team (JVET) to evaluate compressiontechnology designs proposed by their experts in this area. A recentcapture of JVET development is described in the “Algorithm Descriptionof Joint Exploration Test Model 5 (JEM 5)”, JVET-E1001-V2, authored byJ. Chen, E. Alshina, G. Sullivan, J. Ohm, J. Boyce.

Additionally or alternatively, video encoder 20 and video decoder 30 mayoperate according to other proprietary or industry standards thatfunction with the disclosed JVET features. Thus, other standards such asthe ITU-T H.264 standard, alternatively referred to as MPEG-4, Part 10,Advanced Video Coding (AVC), or extensions of such standards. Thus,while newly developed for JVET, techniques of this disclosure are notlimited to any particular coding standard or technique. Other examplesof video compression standards and techniques include MPEG-2, ITU-H.263and proprietary or open source compression formats and related formats.

Video encoder 20 and video decoder 30 may be implemented in hardware,software, firmware or any combination thereof. For example, the videoencoder 20 and decoder 30 may employ one or more processors, digitalsignal processors (DSPs), application specific integrated circuits(ASICs), field programmable gate arrays (FPGAs), discrete logic, or anycombinations thereof. When the video encoder 20 and decoder 30 areimplemented partially in software, a device may store instructions forthe software in a suitable, non-transitory computer-readable storagemedium and may execute the instructions in hardware using one or moreprocessors to perform the techniques of this disclosure. Each of videoencoder 20 and video decoder 30 may be included in one or more encodersor decoders, either of which may be integrated as part of a combinedencoder/decoder (CODEC) in a respective device.

Aspects of the subject matter described herein may be described in thegeneral context of computer-executable instructions, such as programmodules, being executed by a computer, such as the general-purposeprocessors 23 and 33 described above. Generally, program modules includeroutines, programs, objects, components, data structures, and so forth,which perform particular tasks or implement particular abstract datatypes. Aspects of the subject matter described herein may also bepracticed in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote computer storage mediaincluding memory storage devices.

Examples of memory include random access memory (RAM), read only memory(ROM), or both. Memory may store instructions, such as source code orbinary code, for performing the techniques described above. Memory mayalso be used for storing variables or other intermediate informationduring execution of instructions to be executed by a processor, such asprocessor 23 and 33.

A storage device may also store instructions, instructions, such assource code or binary code, for performing the techniques describedabove. A storage device may additionally store data used and manipulatedby the computer processor. For example, a storage device in a videoencoder 20 or a video decoder 30 may be a database that is accessed bycomputer system 23 or 33. Other examples of storage device includerandom access memory (RAM), read only memory (ROM), a hard drive, amagnetic disk, an optical disk, a CD-ROM, a DVD, a flash memory, a USBmemory card, or any other medium from which a computer can read.

A memory or storage device may be an example of a non-transitorycomputer-readable storage medium for use by or in connection with thevideo encoder and/or decoder. The non-transitory computer-readablestorage medium contains instructions for controlling a computer systemto be configured to perform functions described by particularembodiments. The instructions, when executed by one or more computerprocessors, may be configured to perform that which is described inparticular embodiments.

Also, it is noted that some embodiments have been described as a processwhich can be depicted as a flow diagram or block diagram. Although eachmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be rearranged. A process may haveadditional steps not included in the figures.

Particular embodiments may be implemented in a non-transitorycomputer-readable storage medium for use by or in connection with theinstruction execution system, apparatus, system, or machine. Thecomputer-readable storage medium contains instructions for controlling acomputer system to perform a method described by particular embodiments.The computer system may include one or more computing devices. Theinstructions, when executed by one or more computer processors, may beconfigured to perform that which is described in particular embodiments.

As used in the description herein and throughout the claims that follow,“a”, “an”, and “the” includes plural references unless the contextclearly dictates otherwise. Also, as used in the description herein andthroughout the claims that follow, the meaning of “in” includes “in” and“on” unless the context clearly dictates otherwise.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.

Although embodiments have been disclosed herein in detail and inlanguage specific to structural features and/or methodological actsabove, it is to be understood that those skilled in the art will readilyappreciate that many additional modifications are possible in theexemplary embodiments without materially departing from the novelteachings and advantages of the invention. Moreover, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Accordingly, these and all such modifications are intended to beincluded within the scope of this invention construed in breadth andscope in accordance with the appended claims.

The invention claimed is:
 1. A method for using planar mode to predictpixel values for a current coding block having height H, width W, and atop-left (TL) pixel within the current coding block is defined bycoordinates x=0 and y=0, where constructed or reconstructed neighboringpixels form rows of horizontal boundary pixels on an upper side of thecurrent coding block and constructed or reconstructed neighboring pixelsform a column of vertical boundary pixels on a left side of the currentcoding block, the method configured to: calculate an intensity value ofa bottom right neighboring pixel of the current coding block; using thecalculated bottom right neighboring pixel value, calculate intensityvalues for the neighboring pixels in a row along the lower side of thecurrent coding block and in a column along the right side of the currentcoding block: calculate an intensity value of a bottom right neighboringpixel of the current coding block; using the calculated bottom rightneighboring pixel value, calculate intensity values for the neighboringpixels in a row along the lower side of the current coding block and ina column along the right side of the current coding block; calculate ahorizontal predictor P_(h)(x,y) and a vertical predictor P_(v)(x,y) fora pixel at coordinate (x,y) in the current coding block by interpolatingbetween corresponding neighboring pixels in neighboring rows and byinterpolating between corresponding neighboring pixels in neighboringcolumns, respectively: calculating a final planar prediction P(x,y)using unequal weights applied to each of the horizontal and verticalpredictors, respectively, is determined in accordance with:P(x,y)=(A(x,y)*P _(h)(x,y)+B(x,y)*P _(v)(x,y)+c(x,y))/(D(x,y)) whereA(x,y) and B(x,y) are position dependent weighting factors for thehorizontal and vertical predictors, respectively, c(x,y) is a positiondependent rounding factor, and D(x,y) is a position dependent scalingfactor, wherein unequal weights A(x,y) and B(x,y) are assigned forprediction of each pixel within the coding block.
 2. A method for usingplanar mode to predict pixel values for a current coding block havingheight H, width W, and a top-left (TL) pixel within the current codingblock is defined by coordinates x=0 and y=0, where constructed orreconstructed neighboring pixels form rows of horizontal boundary pixelson an upper side of the current coding block and constructed orreconstructed neighboring pixels form a column of vertical boundarypixels on a left side of the current coding block, the method configuredto: calculate an intensity value of a bottom right neighboring pixel ofthe current coding block; using the calculated bottom right neighboringpixel value, calculate intensity values for the neighboring pixels in arow along the lower side of the current coding block and in a columnalong the right side of the current coding block; calculate an intensityvalue of a bottom right neighboring pixel of the current coding block;using the calculated bottom right neighboring pixel value, calculateintensity values for the neighboring pixels in a row along the lowerside of the current coding block and in a column along the right side ofthe current coding block; calculate a horizontal predictor P_(h)(x,y)and a vertical predictor P_(v)(x,y) for a pixel at coordinate (x,y) inthe current coding block by interpolating between correspondingneighboring pixels in neighboring rows and by interpolating betweencorresponding neighboring pixels in neighboring columns, respectively;calculating a final planar prediction P(x,y) using unequal weightsapplied to each of the horizontal and vertical predictors, respectively,is determined in accordance with:P(x,y)=(A(x,y)*P _(h)(x,y)+B(x,y)*P _(v)(x,y)+c(x,y))(D)(x,y)) whereA(x,y) and B(x,y) are position dependent weighting factors for thehorizontal and vertical predictors, respectively, c(x,y) is a positiondependent rounding factor, and D(x,y) is a position dependent scalingfactor, wherein unequal weights A(x,y) and B(x,y) are each assigned to adistance ratio from a reliable neighboring pixel for each of thehorizontal predictor and the vertical predictor.
 3. A method for usingplanar mode to predict pixel values for a current coding block havingheight H, width W, and a top-left (TL) pixel within the current codingblock is defined by coordinates x=0 and y=0, where constructed orreconstructed neighboring pixels form rows of horizontal boundary pixelson an upper side of the current coding block and constructed orreconstructed neighboring pixels form a column of vertical boundarypixels on a left side of the current coding block, the method configuredto: calculate an intensity value of a bottom right neighboring pixel ofthe current coding block; using the calculated bottom right neighboringpixel value, calculate intensity values for the neighboring pixels in arow along the lower side of the current coding block and in a columnalong the right side of the current coding block; calculate an intensityvalue of a bottom right neighboring pixel of the current coding block;using the calculated bottom right neighboring pixel value, calculateintensity values for the neighboring pixels in a row along the lowerside of the current coding block and in a column along the right side ofthe current coding block; calculate a horizontal predictor P_(h)(x,y)and a vertical predictor P_(v)(x,y) for a pixel at coordinate (x,y) inthe current coding block by interpolating between correspondingneighboring pixels in neighboring rows and by interpolating betweencorresponding neighboring pixels in neighboring columns, respectively;calculating a final planar prediction P(x,y) using unequal weightsapplied to each of the horizontal and vertical predictors, respectively,is determined in accordance with:P(x,y)=(A(x,y)*P _(h)(x,y)+B(x,y)*P _(v)(x,y)+c(x,y))(D(x,y)) whereA(x,y) and B(x,y) are position dependent weighting factors for thehorizontal and vertical predictors, respectively, c(x,y) is a positiondependent rounding factor, and D(x,y) is a position dependent scalingfactor, further comprising applying unequal weights to at least one ofthe horizontal predictor and/or the vertical predictor.
 4. A method forusing planar mode to predict pixel values for a current coding blockhaving height H, width W, and a top-left (TL) pixel within the currentcoding block is defined by coordinates x=0 and y=0, where constructed orreconstructed neighboring pixels form rows of horizontal boundary pixelson an upper side of the current coding block and constructed orreconstructed neighboring pixels form a column of vertical boundarypixels on a left side of the current coding block, the method configuredto: calculate an intensity value of a bottom right neighboring pixel ofthe current coding block; using the calculated bottom right neighboringpixel value, calculate intensity values for the neighboring pixels in arow along the lower side of the current coding block and in a columnalong the right side of the current coding block; calculate an intensityvalue of a bottom right neighboring pixel of the current coding block;using the calculated bottom right neighboring pixel value, calculateintensity values for the neighboring pixels in a row along the lowerside of the current coding block and in a column along the right side ofthe current coding block; calculate a horizontal predictor P_(h)(x,y)and a vertical predictor P_(v)(x,y) for a pixel at coordinate (x,y) inthe current coding block by interpolating between correspondingneighboring pixels in neighboring rows and by interpolating betweencorresponding neighboring pixels in neighboring columns, respectively;calculating a final planar prediction P(x,y) using unequal weightsapplied to each of the horizontal and vertical predictors, respectively,is determined in accordance with:P(x,y)=(A(x,y)*P _(h)(x,y)+B(x,y)*P _(v)(x,y)+c(x,y))/(D(x,y)) whereA(x,y) and B(x,y) are position dependent weighting factors for thehorizontal and vertical predictors, respectively, c(x,y) is a positiondependent rounding factor, and D(x,y) is a position dependent scalingfactor, further comprising applying unequal weights at horizontal andvertical predictor computations.
 5. A method for using planar mode topredict pixel values for a current coding block having height H, widthW, and a top-left (TL) pixel within the current coding block is definedby coordinates x=0 and y=0, where constructed or reconstructedneighboring pixels form rows of horizontal boundary pixels on an upperside of the current coding block and constructed or reconstructedneighboring pixels form a column of vertical boundary pixels on a leftside of the current coding block, the method configured to: calculate anintensity value of a bottom right neighboring pixel of the currentcoding block; using the calculated bottom right neighboring pixel value,calculate intensity values for the neighboring pixels in a row along thelower side of the current coding block and in a column along the rightside of the current coding block; calculate an intensity value of abottom right neighboring pixel of the current coding block; using thecalculated bottom right neighboring pixel value, calculate intensityvalues for the neighboring pixels in a row along the lower side of thecurrent coding block and in a column along the right side of the currentcoding block; calculate a horizontal predictor P_(h)(x,y) and a verticalpredictor P_(v)(x,y) for a pixel at coordinate (x,y) in the currentcoding block by interpolating between corresponding neighboring pixelsin neighboring rows and by interpolating between correspondingneighboring pixels in neighboring columns, respectively: calculating afinal planar prediction P(x,y) using unequal weights applied to each ofthe horizontal and vertical predictors, respectively, is determined inaccordance with:P(x,y)=(A(x,y)*P _(h)(x,y)+B(x,y)*P _(v)(x,y)+c(x,y))/(D(x,y)) whereA(x,y) and B(x,y) are position dependent weighting factors for thehorizontal and vertical predictors, respectively, c(x,y) is a positiondependent rounding factor, and D(x,y) is a position dependent scalingfactor, using bottom right position adjustment and unequal weightassignment in combination.
 6. A method for predicting pixel values eachof which having a respective position x, y for a current coding blockbased upon neighboring pixel locations on an upper side of the currentcoding block and on a left side of the current coding block the methodconfigured to: calculate a first predictor for a pixel in the currentcoding block based upon a first value which is determined by a summationof (i) a height of said coding block, (ii) minus 1, (iii) minus y; asecond value determined by said first value multiplied by a value of apixel location of said pixel locations on said upper side of the currentcoding block at horizontal position x; a third value which is determinedby a summation of (i) y, (ii) plus 1; a fourth value determined by saidthird value multiplied by a value of a pixel location of pixel locationson said left side of the current coding block set at a vertical positionof said height of said coding block; said first predictor based upon asummation of said second value and said fourth value, the summation ofwhich is adjusted by a first predictor factor; calculate a secondpredictor, different from the first predictor, for the pixel in thecurrent coding block based upon a fifth value which is determined by asummation of (i) a width of said coding block, where said height of saidcoding block is different than said width of said coding block, (ii)minus 1, (iii) minus x; a sixth value determined by said fifth valuemultiplied by a value of a pixel location of said pixel locations onsaid left side of the current coding block at a vertical position y; aseventh value which is determined by a summation of (i) x, (ii) pus 1;an eighth value determined by said seventh value multiplied by a valueof a pixel location of said pixel locations on said upper side of thecurrent coding block at a horizonal position of said width of saidcoding block; said second predictor based upon a summation of said sixthvalue and said eighth value, the summation of which is adjusted by asecond predictor factor, and derive a prediction pixel value from thesum of (i) the first predictor and (ii) the second predictor and (iii)an adjustment factor, the sum of which is modified by a scaling factor,and wherein a plurality of prediction pixel values make up a predictionblock.
 7. The method of claim 6 wherein deriving the prediction pixelvalue comprises averaging the first and second predictors.
 8. The methodof claim 6 wherein said value of said pixel location of said pixellocations on said upper side of the current coding block at horizontalposition x is a luminance value.
 9. The method of claim 6 wherein saidvalue of said pixel location of said pixel locations on said left sideof the current coding block at said vertical position of said height ofsaid coding block is a luminance value.
 10. The method of claim 6wherein said value of said pixel location of said pixel locations onsaid left side of the current coding block at said vertical position yis a luminance value.
 11. The method of claim 6 wherein said value ofsaid pixel location of said pixel locations on said upper side of thecurrent coding block at said horizonal position of said width of saidcoding block is a luminance value; said value of said pixel location ofsaid pixel locations on said upper side of the current coding block athorizontal position x is a luminance value; said value of said pixellocation of said pixel locations on said left side of the current codingblock at said vertical position of said height of said coding block is aluminance value; said value of said pixel location of said pixellocations on said left side of the current coding block at said verticalposition y is a luminance value, and said value of said pixel locationof said pixel locations on said upper side of the current coding blockat said horizonal position of said width of said coding block is aluminance value.
 12. The method of claim 6 wherein said neighboringpixel locations on said upper side of the current coding block isarranged as a group of pixel locations.
 13. The method of claim 12wherein said neighboring pixel locations on said left side of thecurrent coding block is arranged as a group of pixel locations.
 14. Themethod of claim 6 wherein said neighboring pixel locations on said upperside of the current coding block is arranged as a set of pixellocations.
 15. The method of claim 14 wherein said neighboring pixellocations on said left side of the current coding block is arranged as aset of pixel locations.
 16. The method of claim 6 wherein saidneighboring pixel locations on said upper side of the current codingblock is arranged as an array of pixel locations.
 17. The method ofclaim 16 wherein said neighboring pixel locations on said left side ofthe current coding block is arranged as an array of pixel locations.